CN102124465B - Content recommendation system, content recommendation method, content recommendation device, program, and information storage medium - Google Patents
Content recommendation system, content recommendation method, content recommendation device, program, and information storage medium Download PDFInfo
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/60—Information retrieval; Database structures therefor; File system structures therefor of audio data
- G06F16/63—Querying
- G06F16/635—Filtering based on additional data, e.g. user or group profiles
- G06F16/637—Administration of user profiles, e.g. generation, initialization, adaptation or distribution
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/60—Information retrieval; Database structures therefor; File system structures therefor of audio data
- G06F16/68—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/60—Information retrieval; Database structures therefor; File system structures therefor of audio data
- G06F16/68—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/683—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/907—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
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Abstract
It is possible to recommend various types of contents corresponding to various preferences of a user. The invention provides a content recommendation system includes: an attribute value storage means which stores one or more attribute values of one or more attributes for each of the contents; a preference distribution storage means which stores a user preference degree for each of the attributes values for each of the attributes; an attribute value acquisition means which acquires an attribute value of at least one attribute for each of the contents owned by the user; a preference distribution update means which updates the storage contents in the preference distribution storage means in accordance with the attribute value acquired by the attribute value acquisition means; a condition determination means which selects an attribute value of one or more attributes in accordance with a probability based on the user preference degree stored in the preference distribution storage means and determines the condition of the attribute value of the one or more attributes in accordance with the selected attribute value; and a content selection means which selects some or all of the contents in accordance with the condition determined by the condition determination means.
Description
Technical field
The present invention relates to content recommendation system, content recommendation method, content recommendation device, program and information storage medium, and relate more specifically to commending contents technology.
Background technology
In recent years, user can utilize such as the communication network of the Internet and enjoy the expectation content from selecting among a large amount of contents.Owing to there being a large amount of available contents, so proposed multiple recommended technology.For example, known a kind of technology, it presents to user's (referring to No. 2006-58947th, Japanese Patent Application Publication) for the content of the type of retrieval user preference and by the content finding.
Summary of the invention
But, the not necessarily content of one type of preference only of user, and can enjoy polytype content on certain proportion.For example, conventionally enjoy rock'n'roll user and enjoy sometimes classical music music.
In view of above problem, make the present invention, and the object of this invention is to provide and can recommend all kinds content to meet user's content recommendation system, content recommendation method, program and the information storage medium of various preferences.
In order to overcome the above problems, content recommendation system according to the present invention comprises: property value memory storage, and it is for each property value of each one or more attribute of storage for multiple contents; Preference distribution memory storage, it is for storing the user preference degree about each property value of each attribute; Property value acquisition device, it is for the property value of each at least one attribute of acquisition of one or more content of having for user; Preference distribution updating device, it updates stored in the content in preference distribution memory storage for the property value based on being obtained by property value acquisition device; Condition determining device, the probability of its user preference degree based on being stored in preference distribution memory storage for basis is selected the property value of one or more attribute, and determines the condition of the property value of one or more attribute according to selected property value; Content selecting apparatus, its condition of being determined by condition determining device for basis is selected some or all of multiple contents; And content presents device, it is for presenting to user by the content of being selected by content selecting apparatus.
In addition, content recommendation method according to the present invention comprises: property value obtaining step, the property value of each at least one attribute of acquisition of one or more content wherein having for user; Preference distribution step of updating, the wherein property value based on obtaining in property value obtaining step, update stored in the content in preference distribution memory storage, this preference distribution memory device stores is about the user preference degree of each each property value of one or more attribute of content; Condition determining step, wherein selects the property value of one or more attribute according to the probability based on being stored in the user preference degree in preference distribution memory storage, and determines the condition of the property value of one or more attribute according to selected property value; Content choice step, wherein basis definite condition in condition determining step is selected some or all of multiple contents; And content rendering step, wherein the content of selecting is presented to user in content choice step.
According to an aspect of the present invention, property value acquisition device is for being stored in explicitly each content in predetermined computation machine and obtaining at least one property value of attribute with user.
In addition, content recommendation device according to the present invention comprises: preference distribution memory storage, and it is for storing the user preference degree about each property value of the attribute of content; Property value acquisition device, it is for the property value of each acquisition attribute of one or more content of having for user; Preference distribution updating device, it updates stored in the content in preference distribution memory storage for the property value based on being obtained by property value acquisition device; Property value selecting arrangement, it is for selecting the property value of attribute according to the probability of user preference degree based on being stored in preference distribution memory storage; List acquisition device, it is for obtaining the list of the content from selecting among multiple contents according to the condition of the property value of attribute based on being selected by property value selecting arrangement.
In addition, content recommendation method according to the present invention comprises: property value obtaining step, and it is for the property value of each acquisition predetermined attribute of one or more content of having for user; Preference distribution step of updating, it updates stored in the content in preference distribution memory storage for the property value based on being obtained by property value acquisition device, and this preference distribution memory device stores is about the user preference degree of each property value of attribute; Property value is selected step, and it is for selecting the property value of attribute according to the probability of user preference degree based on being stored in preference distribution memory storage; List obtaining step, it is for according to based on select the condition of the property value of the attribute selected of step to obtain the list of the content from selecting among multiple contents at property value.
In addition, program according to the present invention is used as computing machine: preference distribution memory storage, and it is for storing the user preference degree about each property value of the attribute of content; Property value acquisition device, it is for the property value of each acquisition attribute of one or more content of having for user; Preference distribution updating device, it updates stored in the content in preference distribution memory storage for the property value based on being obtained by property value acquisition device; Property value selecting arrangement, it is for selecting the property value of attribute according to the probability of user preference degree based on being stored in preference distribution memory storage; List acquisition device, it is for obtaining the list of the content from selecting among multiple contents according to the condition of the property value of attribute based on being selected by property value selecting arrangement.
In addition, the such program of information storage medium stores according to the present invention, this program is used as computing machine: preference distribution memory storage, it is for storing the user preference degree about each property value of the attribute of content; Property value acquisition device, it is for the property value of each acquisition attribute of one or more content of having for user; Preference distribution updating device, it updates stored in the content in preference distribution memory storage for the property value based on being obtained by property value acquisition device; Property value selecting arrangement, it is for selecting the property value of attribute according to the probability of user preference degree based on being stored in preference distribution memory storage; And list acquisition device, it is for obtaining the list of the content from selecting among multiple contents according to the condition of the property value of attribute based on being selected by property value selecting arrangement.
Accompanying drawing explanation
Fig. 1 illustrates the configured in one piece figure of content recommendation system according to an embodiment of the invention.
Fig. 2 is the hardware configuration of diagram server.
Fig. 3 is the skeleton view of diagram as the outward appearance of the games system of user's set.
Fig. 4 is the hardware configuration of diagram game machine.
Fig. 5 is the arrangement plan of diagram the first metadata.
Fig. 6 is the arrangement plan of diagram the second metadata.
Fig. 7 illustrates the operational flowchart of content recommendation system according to an embodiment of the invention.
Fig. 8 is the figure of indicative icon theme template data.
Fig. 9 is the figure of indicative icon subject matter preferences distributed data.
Figure 10 is the figure of indicative icon property value preference distribution data.
Figure 11 is the arrangement plan of diagram theme group data.
Figure 12 is the figure of indicative icon property value conversion dictionary.
Figure 13 is the FBD (function block diagram) of diagram user's set.
Figure 14 is the FBD (function block diagram) of diagram server and database.
Figure 15 illustrates the operational flowchart of the modification of content recommendation system according to an embodiment of the invention.
Figure 16 is the outside figure of diagram portable game machine.
Figure 17 is the hardware configuration of diagram portable game machine.
Figure 18 is the hardware configuration of diagram general purpose personal computer.
Embodiment
Hereinafter, explain with reference to the accompanying drawings embodiments of the invention.
Fig. 1 illustrates the configured in one piece figure of content recommendation system according to an embodiment of the invention.As shown in the drawing, this content recommendation system 10 is connected to the data communication network 18 such as the Internet, and comprises server 14 (first content recommendation apparatus) and multiple user's set 12 (second content recommendation apparatus) of data communication mutually.Server 14 comprises database 14a.For example, user's set 12 can be mounted in computer system (such as personal computer, computer game system and home server) and the portable machine (such as portable game machine) in every family.User's set 12 access services devices 14, and the user's of user's set 12 list of songs is recommended in reception.User's set 12 request servers 14 provide the song data being included in list, receive song data, and played songs.On the other hand, for example, server 14 is made up of the computer system of all server computers as is known, and recommends the user's of user's set 12 list of songs to each user's set 12 transmission.In addition, server 14 transmits each song data in response to the request of each user's set 12.In this example, the present invention is applied to song recommendations.But the present invention is not limited thereto.To understand, the present invention can be used to the recommendation of various contents, and these contents are for example such as the moving image of film, such as the rest image of picture and such as the document of novel.
Fig. 2 is the figure of the example of the hardware configuration of diagram server 14.As shown in the drawing, server 14 comprises processor 70, storer 71, hard disk drive 73, media drive 74 and communication interface 76, and they are connected to bus 72 with mutual swap data.Storer 71 comprises ROM and RAM.ROM stores various system programs.RAM is mainly for the treatment of the workspace of device 70.Hard disk drive 73 is stored the program of the list for distributing song and the distribution song of recommending, and database 14a is fabricated the list for distributing song and the distribution song of recommending.Media drive 74 is for reading the data that are stored in computer-readable medium 75 (such as CD-ROM and DVD-RAM) or the device that data is write to computer-readable medium 75.Communication interface 76 is the data communication with user's set 12 via communication network 18 controls.Processor 70 carrys out the unit of Control Server 14 according to the program in storer 71, hard disk drive 73 or medium 75 of being stored in.
Subsequently, will explain user's set 12.Fig. 3 is the outside figure of diagram as the computer game system of user's set 12.This computer game system comprises game machine 200, operating means 202 and television indicator 204.Game machine 200 is computer game systems, and it not only carries out games, also carries out the various programs such as web browser and film/music player program.Can be from the various computer-readable medium fetch program such as various CDs, inside or external fixed disk drive and semiconductor memory, or can download through the computer network such as the Internet.Operating means 202 radio communications are connected to game machine 200 or are connected to game machine 200 via line traffic.
Operating means 202 is by unshowned battery-operated, and comprises that multiple buttons and key, user use button and key to operate input.In the time of the button on user's operating operation device 202 and key, content of operation wirelessly or via circuit is transferred to game machine 200.
Operating means 202 has arrow key 216, operating rod (joy stick) 218 and action button group 220.Arrow key 216, operating rod 218 and action button group 220 are disposed on the top surface 222 of shell.The action button 224,226,228 and 230 of carrying out mark Four types with the unlike signal of different colours is to be distinguished from each other them.More specifically, with red circle marking operation button 224, with blue cross spider marking operation button 226, use purple square marking operation button 228, and with Green triangle shape marking operation button 230.The rear surface 232 of the shell of operating means 202 is provided with multiple LED (not shown).
User holds left side handle portion 234b with left hand in the time of operating operation device 202, and holds right side handle portion 234a with the right hand.Arrow key 216, operating rod 218 and action button group 220 are disposed on the top surface 222 of shell, and they can be operated by the user of handle portion 234b on the left of holding with the right hand and left hand and right side handle portion 234a.
Operating means 202 also comprises selects button 240, start button 238 etc.For example, in the time that user indicates game machine 200 start programs and beginning/time-out movie or music, use start button 238.On the other hand, for example, in the time that user is chosen in one of project of the menu showing on television indicator 204, uses and select button 240.
Now, by the internal circuit configuration of explanation game machine 200.As shown in Figure 4, game machine 200 comprises host CPU 300, GPU (Graphics Processing Unit) 302, I/O processor 304, optical disc replay unit 306, primary memory 308, mask rom 310 and Sound Processor Unit 312, as its critical piece.Host CPU 300 comes executive signal processing and the control to various internal parts based on various programs.GPU 302 carries out image processing.I/O processor 304 is carried out interface or the processing between some of parts outside parts and the device in CPU 300 and device.In addition, I/O processor 304 can have the function of executive utility, and game machine 200 is had and the compatibility of other game machine.
Optical disc replay unit 306 reproduces the CD (such as BD, DVD or CD) of storage application program or multi-medium data.Primary memory 308 is stored from the impact damper of the data of disc reading as the workspace of host CPU 300 with for interim.The operating system program that mask rom 310 storages are mainly carried out by host CPU 300 and I/O processor 304.Sound Processor Unit 312 is carried out Audio Signal Processing.
For example, hard disk drive 334 is stored the program that read by optical disc replay unit 306 and the save data of games, or storage is such as the picture obtaining through I/O processor 304, mobile image and music data.Bayonet nut connector 320 is the connectivity ports for such as communication card, external fixed disk drive etc.
These internal parts mainly interconnect by bus 322,324 etc.Host CPU 300 is connected by private bus with GPU 302.In addition, host CPU 300 is connected by high speed BUS with I/O processor 304.Similarly, I/O processor 304, CD/DVD/BD processor 314, mask rom 310, Sound Processor Unit 212, bayonet nut connector 320 and hard disk drive 334 are connected by high speed BUS.
I/O processor 304 carry out be stored in mask rom 310, for the operating system program of I/O processor, with the data I/O of control and operation device 202, storage card 326, USB splicing ear 208, Ethernet (registered trademark) 330, IEEE1394 terminal (not shown) and PC draw-in groove.Control via the interface 232 that comprises multimedia groove and wireless communication port with the data I/O of operating means 202 and storage card 326.
GPU 302 has the function of the geometric transformation engine for carrying out coordinate transform etc. and plays up the function of (rendering) processor.GPU 302 according to provided by host CPU 300 play up instruction and in frame buffer (not shown) drawing image.For example, use 3D figure in the program being stored on CD, GPU 302 calculates polygonal coordinate to form three dimensional object in geometric operation is processed.In addition, GPU 302 is playing up making image in processing, and this image can obtain by taking three dimensional object by virtual camera.Obtained image is write frame buffer by GPU 302.Then GPU 302 outputs to television indicator 204 by the vision signal of the image corresponding to storage.Therefore, image is presented on the screen 204b of television indicator 204.
Sound Processor Unit 312 has ADPCM (adaptive difference pulse code modulation) decoding function, sound signal representational role and signal modulation function.ADPCM decoding function is for the function from generate Wave data with the voice data of ADPCM coding.Sound signal representational role is for generating the function for the sound signal of for example sound effect from the Wave data being stored in sound buffer (this sound buffer is merged in Sound Processor Unit 312 or is connected with Sound Processor Unit 312 is outside).The internal loudspeaker 204a of television indicator 204, the represented sound of 204a output audio signal.Signal modulation function is the function for modulating the Wave data that is stored in sound buffer.
For example, in the time opening game machine 200, read the operating system program for host CPU 300 and I/O processor 304 from mask rom 310.Carry out these operating system programs by host CPU 300 and I/O processor 304.Therefore, all parts of host CPU 300 middle ground control game machines 200.On the other hand, I/O processor 304 is controlled at the element such as controller 202 and storage card 326, and signal I/O between game machine 200.In addition, by executive operating system program, host CPU 300 is carried out the initialization such as operation inspection etc.Then host CPU 300 controls optical disc replay unit 306 with the application program for playing etc. from disc reading.After application program is written into primary memory 308, host CPU 300 executive routines.By executive utility, host CPU 300 is deferred to the operator's who receives by operating means 202 and I/O processor 304 instruction and is controlled GPU 302 and Sound Processor Unit 312, with the generation of the demonstration of control chart picture and sound effect, musical sound etc.
Content recommendation system 10 is applied two kinds of filtrators to select to recommend user's song from many songs in overlapping mode.Fig. 5 is the figure that indicative icon utilizes the first metadata of the first filtrator.Fig. 6 is the figure that indicative icon utilizes the second metadata of the second filtrator.Any one in them is stored in database 14a.As shown in Figure 5, the first metadata comprises the property value of music ID and multiple attributes.Music ID is each information of being recommended many songs of user by content recommendation system 10 for identifying.Prepare the multiple attributes that are suitable for the feature that represents each song in advance, and each song has been provided to the property value of these attributes.About attribute and property value, in the situation that attribute is song style, the example of property value comprises rock music, pop music, classical music music, jazz etc.In the time that attribute is the time of artist's time of being born and artist's debut, the example of property value comprises 1950,1960,1970 etc.Be song while listing the time of welcome list at attribute, the example of property value comprises 1999,2000,2001 etc.In the situation that attribute is artistical nationality, the example of property value comprises Japan, the U.S. etc.In the situation that attribute is artistical sex, the example of property value comprises man and female.The result that the property value of some attributes can be used as the analyzing and processing of being carried out by computing machine is transfused to.But, wish to input most attributes by people.
As shown in Figure 6, the second metadata comprises the characteristic quantity of music ID and multiple features.The example of feature comprises that beat, the song of song comprise the degree of the particular keywords occurring in the degree of the sound with characteristic frequency and the explanatory text of song.The result that these characteristic quantities can be used as the analyzing and processing of being carried out by computing machine is transfused to.In the following description, its component is that the vector of the characteristic quantity of feature is described to proper vector.
In content recommendation system 10, consider user's preference, change successively the condition (property value condition) of property value by random number, make to utilize the first filtrator from many songs, to extract its first metadata and meet the song of property value condition.Subsequently, for extracted each song, calculate the similarity between the preference vector of feature and the proper vector of each song of song that represents user preference.Determine the song of the predetermined number with higher similarity according to the descending of similarity, as the song that will recommend user.Similar to the proper vector of each song, preference vector is that its component is the vector of the characteristic quantity of feature as shown in Figure 6.The proper vector of the song by synthetic user preference, can generate these preference vectors.Similarity between vector can be two angles between vector.In this case, the angle of formation is less, and similarity is higher.According to the present embodiment, various types of songs are presented to user successively, and can continue interested content in recommendation family.
Fig. 7 is the operational flowchart of content recommendation system 10.First, in content recommendation system 10, by user's set 12 choosing a topic data (S201).Subject data comprises theme template ID and is used as the property value of the parameter of the theme template of being identified by theme template ID.As shown in Figure 8, theme template is the template of the condition (property value condition) of the each property value for generating song, and the property value that provides the attribute of being specified by theme template is as parameter, obtains thus the property value condition of song.In Fig. 8, specify the attribute in " time of artist's birth " and " style ", and for example,, to these attributes given " 1980 " and " rock music ", obtain thus the property value condition of indication " time of artist's birth is that 1980 times and style are rock musics ".In above the first filtrator, with reference to the first metadata as shown in Figure 5, and from many songs, select to meet the song of determined property value condition.
Generate multiple theme templates by people in advance, and theme template ID is the information for identifying each theme template.As shown in Figure 9, for each theme template ID, user's set 12 is stored the user preference degree (its degree of user preference) by the theme template of theme template ID mark, that is, and and subject matter preferences distributed data.User's set 12 generates random number, and selects successively each theme template ID according to the probability based on preference.Then, obtain the specified attribute of theme template by selected theme template ID mark, and selected the property value of related attribute.Now also select the property value of each attribute according to the random number based on preference.In other words, as shown in figure 10, for all properties, user's set 12 is stored the user preference degree of each property value, that is, and and property value preference distribution data.User's set 12 generates random number, and selects the property value of appointed attribute according to the probability based on preference.After this, pre-stored user's preference vector and subject data are transferred to server 14 (S202) by user's set 12.
Subsequently, server 14 calculates the similarity between the preference vector receiving from user's set 12 and the proper vector of each song of selecting among step S402, and selects the song (S403) of the predetermined number with higher similarity according to the descending of similarity.The list of songs (S404) of then, replying to user's set 12 the music ID that comprises predetermined number song.In the time that user's set 12 receives music list, user's set 12 is transferred to server 14 (S203) by being included in one of music ID in music list, and server 14 reads the data of the song being identified by music ID from database 14a, and reply these data (S405).User's set 12 is play the data (S204) of replied song, and the internal loudspeaker 204a of television indicator 204,204a output song.Now, may be displayed on the screen 204b of television indicator 204 such as the title of current broadcasting and the information of artistical name.In the time that broadcasting described above is included in the song of all music ID in music list, user's set 12 is carried out the processing of S201 again.
Now, by the functional configuration of explanation user's set 12 and server 14.Figure 13 is the FBD (function block diagram) of user's set 12.Figure 14 is the FBD (function block diagram) of server 14.The functional block shown in these figure by make user's set 12 and server 14 respectively executive routine realize.Previously each program was stored in the readable information storage medium in user's set 12 or server 14, and can be via medium by each installation to user's set 12 and server 14.As an alternative, it can be via data communication network 18 from other downloaded.
As shown in figure 13, user's set 12 comprises theme selected cell 41, subject matter preferences distributed data storage unit 42, subject matter preferences distributed data unit 43, request unit 44, property value determining unit 45, property value preference distribution data storage cell 46, property value preference distribution data unit 47, preference vector storage unit 48, theme template storage unit 49, preference vector unit 50, operating unit 51 and reproducing music unit 52.
First, subject matter preferences distributed data storage unit 42 is mainly made up of hard disk drive 334 or storer 308, and storage subject matter preferences distributed data as shown in Figure 9.Subject matter preferences distributed data unit 43 is mainly made up of host CPU 300 and storer 308.Upgrade subject matter preferences distributed data according to the content of operation that operating unit 501 is carried out.More specifically, for the song or the more songs that are included in the predetermined number the music list of transmitting from server 14, when (1) is not in the time that playback of songs is carried out skip operations (affirming situation 1) to ending and in the time that (2) execution specific operation carrys out the song (situation 2 certainly) of indicating user institute preference, carry out and process the user preference degree being associated with the ID of the template of the music list for generation server 14 with raising.In other words, there are certainly situation 1 or at 2 o'clock about song in the music list being generated by theme template, improved the preference of theme template.On the contrary, for song or more songs of predetermined number, in the time that (1) interrupts the playback (negate situation 1) of song by carrying out skip operations and when (2) carry out specific operation, carry out indicating user not when the song of preference (negating situation 2), carry out and process the user preference degree being associated with ID with reduction.In other words,, when song in the music list about being generated by theme template occurs while negating situation 1 or 2, reduced the preference of theme template.Theme selected cell 41 is mainly made up of host CPU 300 and storer 308.Theme selected cell 41 is with reference to being stored in the subject matter preferences distributed data in subject matter preferences distributed data storage unit 42, to select successively each theme template ID according to the probability based on preference.More specifically, theme selected cell 41 has the scope of the random value being associated with each theme template ID, and the size of scope is set according to preference.Theme selected cell 41 generates random number, and the theme template ID that selects the scope affiliated with this random number to be associated.
Property value preference distribution data storage cell 46 is mainly made up of hard disk drive 334 or storer 308, and storage property value preference distribution data as shown in figure 10.Property value preference distribution data unit 47 is carried out Update attribute value preference distribution data according to the content of operation that operating unit 51 is carried out and the property value that is stored in the music data in user's set 12.More specifically, in the time there is any one of above-mentioned situation certainly in the song of the predetermined number the music list about transmitting from server 14 or more songs, obtain right as the attribute of necessary condition in the property value condition of the music list for generation server 14 and property value, and carry out and process the user preference degree that the property value with being obtained to improve in the property value preference distribution data relevant to the attribute obtaining is associated.On the contrary, in the time that any one of above-mentioned negative situation occurs for the song about predetermined number or more songs, carry out and process to be reduced in the user preference degree being associated with property value in property value preference distribution data.
When any one of above-mentioned situation certainly occurs the song of current broadcasting in about user's set 12, obtain the first metadata of related song from server 14.Then, carry out and process the user preference degree that the property value with being included in the attribute in the first metadata of acquisition to improve in each property value preference distribution data is associated.On the contrary, while there is any one of above-mentioned negative situation, obtain the first metadata of related song from server 14.Then, carry out and process the user preference degree that the property value with being included in the attribute in the first metadata of acquisition to be reduced in each property value preference distribution data is associated.
In addition, property value preference distribution data unit 47 is mainly made up of host CPU 300 and storer 308.That property value preference distribution data unit 47 retrieve stored have user, be arranged in all music datas in hard disk drive 334 and other memory storage in user's set 12 by user, and obtain the first metadata of each song from server 14.Then, carry out and process the user preference degree that the property value with being included in the attribute in the first metadata of acquisition to improve in each property value preference distribution data is associated.By doing like this, the song that each property value preference distribution data can have according to user obtains, and reflects the multiple preference of user about song, thereby can be by various song recommendations to user.In this case, upgrade each property value preference distribution data according to the music data being stored in the memory storage being arranged in user's set 12.In the time that the music data of the song that user has is stored in other computing machines of data communication network 18 grades that are connected to server 14, can upgrade each property value preference distribution data according to the music data that is stored in the user in other computing machine.
Theme template storage unit 49 is mainly made up of hard disk drive 334 or storer 308, and stores many theme templates as shown in Figure 8.Property value determining unit 45 is mainly made up of host CPU 300 and storer 308.In the time that property value determining unit 45 receives theme template ID from theme selected cell 41, property value determining unit 45 reads the theme template by theme template ID mark from theme template storage unit 49, to check the attribute (attribute-name) of wherein specifying.Then, dependency value preference distribution data storage cell 46 reads the property value preference distribution data of appointed each attribute.After this, property value determining unit 45 is with reference to the property value preference distribution data that read, and selects the property value of each attribute according to the probability based on preference.More specifically, property value determining unit 45 has the scope of the random value being associated with each property value, and the size of scope is set according to preference.Property value determining unit 45 generates random number, and the property value that is associated of scope under selection and random number.
Preference vector storage unit 48 is mainly made up of hard disk drive 334 or storer 308, and storage user's preference vector.Request unit 44 is mainly made up of host CPU 300, storer 308, I/O processor 304 and Ethernet 330.Make preference vector and subject data paired, and be transferred to server 14.Subject data comprises the property value of each attribute that the theme template ID that exports from theme selected cell 41 and dependency value determining unit 45 export.
Reproducing music unit 52 is mainly made up of host CPU 300, storer 308, Sound Processor Unit 312, I/O processor 304 and Ethernet 330.Reproducing music unit 52 receives music list from server 14, and in order the music ID being included in music list is transferred to server 14.Then, reproducing music unit 52 receives the music data corresponding to music ID from server 14, and reproducing music data.Operating unit 51 is configured to comprise operating means 202.Operating unit 51 is used to indicate reproducing music unit 52 to skip the song of current broadcasting, and indicating clearly this song is the song that user likes, or to indicate clearly this song be the song that user does not like.
Subsequently, as shown in figure 14, server 14 comprises server main body 14b and database 14a.Server main body 14b is furnished with request reception unit 21, comprises that subject data changes the first filtrator 22 of unit 22a and music list generation unit 22b, the second filtrator 23, music list reply unit 24 and music distribution unit 25.On the other hand, database 14a is furnished with theme group data storage cell 31, theme template storage unit 32, property value conversion dictionary storage unit 33, the first metadata storage unit 34, the second metadata storage unit 35 and music data storage unit 36.
First, theme group data storage cell 31 is mainly made up of hard disk drive 73 or storer 71, and storage theme group data as shown in figure 11.Theme template storage unit 32 is stored theme template as shown in Figure 8.Property value conversion dictionary storage unit 33 is mainly made up of hard disk drive 73 or storer 71, and storage property value conversion dictionary as shown in figure 12.The first metadata storage unit 34 is mainly made up of hard disk drive 73 or storer 71, and storage the first metadata as shown in Figure 5.In addition, the second metadata storage unit 35 is mainly made up of hard disk drive 73 or storer 71, and storage the second metadata as shown in Figure 6.In addition, music data storage unit 36 is mainly made up of hard disk drive 73, and the data (music data) of many songs of for example, being associated with the identification information (music ID) of song of storage.
Music list generation unit 22b is mainly made up of processor 70 and storer 71.Music list generation unit 22b reference is stored in the first metadata in the first metadata storage unit 34, and selects the song of the property value of the each attribute receiving.Then, music list generation unit 22b exports the list of the music ID of these songs.
The second filtrator 23 is mainly made up of processor 70 and storer 71, and receives the list of music ID from music list generation unit 22b, and receives preference vector from request reception unit 21.Then, read explicitly with the each music ID being included in list the proper vector being stored in the second metadata storage unit 35, and calculate the similarity between each proper vector and preference vector.Then, according to similarity, song is classified, and select the song of predetermined number according to the descending of similarity.Then, export the list of the ID of selected song.Music list is replied unit 24 user's set 12 is replied to obtained list.
Content recommendation system 10 as above is applied the first filtrator 22 and the second filtrator 23 to select some of many contents in overlapping mode, and user's set 12 is reproduced in order and export selected content.The first filtrator 22 is selected song according to the property value condition being generated successively by theme selected cell 41, property value determining unit 45 and subject data change unit 22a.The second filtrator 23 is selected song according to the similarity between the proper vector of each song and user's preference vector.Therefore, select simply compared with the situation of song with utilizing preference vector, can be by various song recommendations to user.Particularly, owing to having determined property value condition based on random number, so can be in unimaginable mode by song recommendations to user.In addition, the first filtrator 22 extracts some of many songs, and after this, some songs is carried out to the processing of the second filtrator 23.Therefore, can reduce the calculated amount of recommending song required.
In addition, generating subject data according to the random choosing a topic template of subject matter preferences distributed data with while selecting property value at random according to property value preference distribution data.Therefore, can generate various subject datas according to user's preference.Owing to utilizing these various subject datas to select song, so can be by various song recommendations to user.In addition, subject data changes unit 22a use theme group data and property value is changed dictionary so that the subject data receiving from user's set 12 is become to other subject data.Therefore, the first filtrator 22 can be selected song according to the theme template and the property value that are not also stored in user's set 12.Therefore, can improve unexpected property and the diversity in song selection.
In addition, in content recommendation system 10, the property value of the song having according to user carrys out Update attribute value preference distribution data.Therefore,, even if user has various music preferences, also can recommend various songs according to various preferences.
The present invention is not limited to above embodiment.Can carry out various modifications.For example, commending contents processing can be can't help user's set 12 and server 14 and shared.A computing machine can select to recommend user's song.On the contrary, commending contents processing can be shared by many computing machines.In the above description, the content of the operation based on utilizing operating unit 51 to carry out is carried out Update attribute value preference distribution data.But the song that can only have based on user is carried out Update attribute value preference distribution data.As an alternative, substitute and utilize the second filtrator 23, the song of being selected in statu quo can be recommended to user by the first filtrator 22.
As an alternative, first, server 14 can utilize the second filtrator to select song, and after this, user's set 12 can utilize the first filtrator further to select song (reduction) among selection result.Figure 15 is the operational flowchart of this modification of diagram.According in the content recommendation system 10 of this modification, first, user's preference vector is transferred to server 14 (S501) by user's set 12.
Server 14 calculates at the preference vector receiving from user's set 12 and is stored in the similarity between each proper vector of all or some songs musical database 36, and selects the song (S601) of the predetermined number with higher similarity according to the descending of similarity.Then the music list that, comprises the music ID of the song of predetermined number is responded to user's set 12 (S602).In the time that user's set 12 receives music list, choosing a topic data (S502).Method for choosing a topic data is identical with Fig. 7.Then, user's set 12 obtains property value condition based on the subject data of selecting in S502.After this, in reference the first metadata, among being included in the song the music list receiving from server 14, select to meet the song of the property value condition obtaining, and the music ID of selected song is included in (S503) in music list.After this, last, be included in one of music ID in music list and be transferred to server 14 (S504).Then, server 14 reads the data of the song being identified by music ID from database 14a, and replys data (S603).User's set 12 reproduces the data (S505) of replied song, and the internal loudspeaker 204a of television indicator 204,204a outputting music.In the time that reproduction is included in the song of all music ID in music list by this way, user's set 12 is carried out the processing of S201 again.In the time that such execution is processed, can reduce the load of the processing of server 14.
As an alternative, the selection of the song based on the first metadata can be shared by user's set 12 and server 14.For example, when property value condition comprise multiple AND (with) when condition (productive set), server 14 can select to meet the song of some condition, and user's set 12 can select to meet the song of all the other conditions among being included in the song selection result.User's set 12 can be determined the song being included in the selection result being provided by server 14 or the order that further meets the reproduction of the song of all the other conditions based on all the other conditions.
As an alternative, the song of being reproduced by user's set 12 or server 14 can be got rid of the song that user does not like.Utilizing operating means 202 executable operations at song reproduction period negates that assessment (indicating user is not liked the concrete operations of song) or executable operations are suspended the instruction of reproducing to provide to indicate clearly, and user's set 12 is included in the music ID of song and is stored in not liking in music list in hard disk drive 334.Comprise while being listed in the music ID that does not like the song music list when user's set 12 receives music list and music list from server, can not allow to reproduce song.As an alternative, when each user do not like music list managed by server 14 and generate music list in server 14 time, music list can not comprise and is listed in the music ID that does not like the song in music list.
Music list is not liked in alternative generation, and sorter (SVM: support vector machine) can be used to determine that user likes still not liking each song.For example, sorter software is installed to user's set 12.Utilizing operating means 202 executable operations at song reproduction period negates that assessment (indicating user is not liked the concrete operations of song) or executable operations are suspended the instruction of reproducing to provide to indicate clearly, or utilize operating means 202 executable operations to like assessment (indicating user is liked the concrete operations of song) or played songs not to have until song finishes interruption to indicate clearly at song reproduction period, this fact is imported into sorter, makes sorter study user's preference.Then, for each song with the music ID being included in the music list of transmitting from server 14, sorter can determine whether user likes song, and can not make the song that user does not like reproduce.
User's set 12 can utilize various hardware to realize.For example, user's set 12 can utilize portable game machine to realize.Figure 16 illustrates the outward appearance of portable game machine.Portable game machine 400 reproduces the digital content such as mobile image, rest image and music, and carries out games etc.Each content is that the exterior storage medium from dismantling from portable game machine 400 reads, or downloads through data communication.Little CD 402 and the storage card 426 such as UMD (universal media laser disc) according to the exterior storage medium of the present embodiment.CD 402 and storage card 426 are mounted respectively on the drive assembly (not shown) being arranged in portable game machine 400.CD 402 not only can be stored music data and Still image data, and can store the motion image data (such as film) with relatively large size of data.Storage card 426 is the mini memory cards that can also be detachably mounted in digital camera or mobile phone.The main storage of storage card 426 utilizes other Still image data of installing generation, moving image data, voice data etc. or the data with other device exchange by user.
Portable game machine 400 is provided with liquid crystal display 404, operating unit member such as arrow key 416, simulating rod 418, button 420 etc.User with the hands holds right-hand member and the left end of portable game machine 400.Arrow key 416 or simulating rod 418 mainly operate to specify up/down/left/right to move by left hand thumb.Button 420 mainly uses to provide various instructions by right hand thumb.Be different from arrow key 416 and button 420, initial button 436 is arranged on following position: in the time with the hands holding the left end of portable game machine 400 and right-hand member, any finger can not be pressed into this position, thereby prevents faulty operation.The reproduction screen of liquid crystal display 404 display menu screens and each content.Portable game machine 400 is also provided with the communication function through USB port and WLAN realization, for carrying out exchanges data with other device that uses USB port and WLAN.Portable game machine 400 is also provided with selects button 440, start button 438 etc.For example, in the time that user indicates portable game machine 400 start game, beginning content (such as film or music) reproduction or suspend the playback of game or film or music, use start button 438.Select to be presented at the menu item in liquid crystal display 404 with selecting button 440.
Figure 17 illustrates the internal circuit configuration of portable game machine 400.Portable game machine 400 comprises: comprise CPU 541, the control system 540 of peripherals etc., comprise the graphics system 550 for GPU 552 grades at frame buffer 553 drawing images, comprise for generating musical sound, the audio system 560 of SPU (sound processing unit) 561 grades of sound effect etc., be used for the CD control module 570 of the CD 402 of control store application program, wireless communication unit 580, interface unit 590, operation input block 502, be connected to each bus of above unit etc.
Under the control of control system 540, display unit 3 shows the image being stored in frame buffer 553.Under the control of CPU 541, image decoder 554 decode stored in primary memory 543, under the control of CPU 541 by the orthogonal transformation compression such as discrete cosine transform and the rest image of coding or the view data of mobile image, and store the view data of decoding into primary memory 543.
Peripheral unit control unit 542, the primary memory 543 being formed by RAM and ROM 544 that control system 540 comprises CPU 541, controls and interrupt control for carrying out for example directmemoryaccess (DMA) transmission.ROM 544 storages are used for the program of such as the operating system etc. of each unit of controlling portable game machine 400.CPU 541 is by reading the operating system being stored in ROM 544 primary memory 543 and carry out the operating system reading and control whole portable game machine 400.User's set 12 can also utilize portable game machine 400 as above to realize.
User's set 12 can also utilize general purpose personal computer to realize.Figure 18 illustrates the internal circuit configuration of general purpose personal computer.
General purpose personal computer comprises host CPU 600, Graphics Processing Unit 602, input block 604, output unit 605, driver 614, primary memory 608 and ROM 610, as its critical piece.Host CPU 600 is processed and inner composed component based on carry out control signal such as the program of operating system and application program.GPU 602 carries out image processing.
These unit are connected to each other via bus 622.Bus 622 is also connected to input/output interface 632.Input/output interface 632 be connected to such as the storage unit 634 of hard disk and nonvolatile memory, comprise display and loudspeaker output unit 605, comprise the input block 604 of keyboard, mouse, microphone etc., such as the external apparatus interface of USB and IEEE1394, comprise for the communication unit 630 of the network interface of wired or wireless LAN and for driving the driver 614 such as the detachable recording medium 626 of disk, CD or semiconductor memory.
Claims (5)
1. a content recommendation system, comprising:
Property value memory storage, it is for each property value of each one or more attribute of storage for multiple contents;
Preference distribution memory storage, it is for storing the user preference degree about each property value of each described attribute;
Property value acquisition device, it is for the property value of each at least one attribute of acquisition of one or more content of having for user;
Preference distribution updating device, it,, for the described property value based on being obtained by described property value acquisition device, updates stored in the content in described preference distribution memory storage;
Condition determining device, the probability of its user preference degree based on being stored in described preference distribution memory storage for basis is selected the property value of one or more attribute, and determines the condition of the property value of one or more attribute according to selected property value;
Content selecting apparatus, its described condition of being determined by described condition determining device for basis is selected some or all of described multiple contents;
Filtration unit, it is for for the selected each content of described content selecting apparatus, obtain the proper vector of the feature relevant to this content, and according to obtained proper vector and represent the similarity between the preference vector of feature of content of user preference, some or all of described multiple contents of selecting from described content selecting apparatus are filtered; And
Content presents device, and it is for presenting to described user by the described content after described filtration unit filters.
2. content recommendation system according to claim 1,
Wherein, described property value acquisition device is for being stored in explicitly each described content in predetermined computation machine and obtaining the property value of described at least one attribute with described user.
3. a content recommendation method, comprising:
Property value obtaining step, the property value of each at least one attribute of acquisition of one or more content wherein having for user;
Preference distribution step of updating, the wherein described property value based on obtaining in described property value obtaining step, update stored in the content in preference distribution memory storage, described preference distribution memory device stores is about the user preference degree of each each property value of one or more attribute of content;
Condition determining step, wherein select the property value of one or more attribute according to the probability based on being stored in the user preference degree in described preference distribution memory storage, and determine the condition of the property value of one or more attribute according to selected property value;
Content choice step, the described condition that wherein basis is determined in described condition determining step is selected some or all of described multiple contents;
Filtration step, wherein for the selected each content of described content choice step, obtain the proper vector of the feature relevant to this content, and according to obtained proper vector and represent the similarity between the preference vector of feature of content of user preference, some or all of described multiple contents of selecting from described content choice step are filtered; And
Content rendering step, wherein presents to described user by the described content after described filtration step filters.
4. a content recommendation device, comprising:
Preference distribution memory storage, it is for storing the user preference degree about each property value of the attribute of content;
Property value acquisition device, it is for the property value of each acquisition attribute of one or more content of having for user;
Preference distribution updating device, it,, for the described property value based on being obtained by described property value acquisition device, updates stored in the content in described preference distribution memory storage;
Property value selecting arrangement, it is for selecting the property value of attribute according to the probability of user preference degree based on being stored in described preference distribution memory storage;
Content selecting apparatus, the condition of its property value of the described attribute based on being selected by described property value selecting arrangement for basis is selected some or all of described multiple contents;
Filtration unit, it is for for the selected each content of described content selecting apparatus, obtain the proper vector of the feature relevant to this content, and according to obtained proper vector and represent the similarity between the preference vector of feature of content of user preference, some or all of described multiple contents of selecting from described content selecting apparatus are filtered;
List acquisition device, it is for obtaining the list of the content after described filtration unit filters.
5. a content recommendation method, comprising:
Property value obtaining step, the property value of each acquisition predetermined attribute of one or more content wherein having for user;
Preference distribution step of updating, wherein the described property value based on being obtained by property value acquisition device, updates stored in the content in preference distribution memory storage, and described preference distribution memory device stores is about the user preference degree of each property value of attribute;
Property value is selected step, wherein selects the property value of described attribute according to the probability based on being stored in the user preference degree in described preference distribution memory storage;
Content choice step, its condition according to the property value of the described attribute based on selecting in described property value selection step is selected some or all of described multiple contents;
Filtration step, it is for selected each content in described content choice step, obtain the proper vector of the feature relevant to this content, and according to obtained proper vector and represent the similarity between the preference vector of feature of content of user preference, some or all of described multiple contents of selecting from described content choice step are filtered; And
List obtaining step, wherein obtains the list of the content after described filtration step filters.
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| CN102124465A (en) | 2011-07-13 |
| US20150006539A1 (en) | 2015-01-01 |
| JP5510329B2 (en) | 2014-06-04 |
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| US8856159B2 (en) | 2014-10-07 |
| US10459948B2 (en) | 2019-10-29 |
| WO2010027033A1 (en) | 2010-03-11 |
| JPWO2010027033A1 (en) | 2012-02-02 |
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